My random numbers that output, output in the same sequence every time I run my game. Why is this happening?
I have
#include <cstdlib>
and am using this to generate the random numbers
randomDiceRollComputer = 1 + rand() % 6;
You need to seed your random number generator:
Try putting this at the beginning of the program:
srand ( time(NULL) );
Note that you will need to #include <ctime>.
The idea here is to seed the RNG with a different number each time you launch the program. By using time as the seed, you get a different number each time you launch the program.
You need to give the randum number generator a seed. This can be done by taking the current time, as this is hopefully some kind of random.
#include <cstdlib>
#include <ctime>
using namespace std;
int main()
{
int r;
srand(time(0));
r = rand();
return 0;
}
The rand() function is specifically required to produce the same sequence of numbers when seeded with a given seed (by calling srand()); each possible seed value specifies a sequence. And if you never call srand(), you get the same sequence you would have gotten by calling srand(1) before any call to rand().
(This doesn't apply across different C or C++ implementations.)
This can be useful for testing purposes. If there's a bug in your program, for example, you can reproduce it by re-running it with the same seed, guaranteeing that (barring other unpredictable behaviors) you'll get the same sequence of pseudo-random numbers.
Calling srand(time(NULL)) is the usual recommended way to get more or less unpredictable pseudo-random numbers. But it's not perfect. If your program runs twice within the same second, you'll probably get the same sequence, because time() (typically) has a resolution of 1 second. And typical `rand() implementations are not good enough for cryptographic use; it's too easy for an attacker to guess what numbers you're going to get.
There are a number of other random number implementations. Linux systems have two pseudo-devices, /dev/random and /dev/urandom, from which you can read reasonably high-quality pseudo-random byte values. Some systems might have functions like random(), drand48(), and so forth. And there are numerous algorithms; I've heard good things about the Mersenne Twister.
For something like a game, where you don't expect or care about players trying to cheat, srand(time(NULL)) and rand() is probably good enough. For more serious purposes, you should get advice from someone who knows more about this stuff than I do.
Section 13 of the comp.lang.c FAQ has some very good information about pseudo-random number generation.
Pseudorandom number generators take a starting number, or seed, and then generate the next number in the sequence from this. That's why they're called pseudorandom, because if they always use the same starting value, they will generate the same sequence of numbers like the C standard lib generator does. This can be fixed by giving the generator a starting value that will change the next time the program is run like the current time.
Anyway, the code you're looking for like others have said is:
srand(time(0)); //Seed the generator, give it a starting value
Related
I have been learning recently how to program games in c++ from a beginner book, and i reached a lesson where i have to make a game in where i have to guess the computer's random picked number, and i have to use this line of code:
srand(static_cast<unsigned int>(time(0)));
variable=rand();
I obviously use iostream cstdlib and ctime.I don't really understand how this works.How is it picking the time and date, and by what rules is it converting into an unsigned int. Basically, how those functions work.
Thank you!
1. About time()
time (or better std::time in C++) is a function that returns some integer or floating point number that represents the current time in some way.
Which arithmetic type it actually returns and how it represents the current time is unspecified, however, most commonly you will get some integer type that holds the seconds since begin of the Unix epoch.
2. About srand()
srand is a function that uses its argument (which is of type unsigned int), the so called seed, to set the internal state of the pseudo number generator rand. When I write random in the rest of this answer, read pseudo random.
Using a different seed will in general result in a different sequence of random numbers produced by subsequent calls to rand, while using the same seed again will result in the exactly same sequence of random numbers.
3. Using time() to seed rand()
If we do not want to get the same random numbers every time we run the program, we need some seed that is different on each run. The current time is a widely used source for such a seed as it changes constantly.
This integer (or whatever else time returned) representing the current time is now converted to unsigned int with a static_cast. This explicit cast is not actually needed as all arithmetic types convert to unsigned int implicitly, but the cast may silence some warnings. As time goes by, we can expect the resulting unsigned int and thus the sequence of random numbers produced by rand to change.
4. Pitfalls
If, as is common, time returns the number of seconds since the beginning of the Unix epoch, there are three important things to note:
The sequence you produce will be different only if at least a second has passed between two invocations.
Depending on the actual implementation, the resulting sequences may start of kind of similar if the time points used to seed rand are close to each other (compared to time since Epoch). Afaik, this is the case in MSVC's implementation. If that is problematic, just discard the first couple of hundred or thousand values of the sequence. (As I have learned by now, this does not really help much for poor RNGs as commonly used for rand. So if that is problematic, use <random> as described below.)
Your numbers are not very random in the end: If someone knows when your call to srand occurred, they can derive the entire sequence of random numbers from that. This has actually led to a decryption tool for a ransom ware that used srand(time(0)) to generate its "random" encryption key.
Also, the sequence generated by rand tends to have poor statistical properties even if the seed was good. For a toy program like yours, that is probably fine, however, for real world use, one should be aware of that.
5. The new <random>
C++11 introduced new random number facilities that are in many ways superior to the old rand based stuff. They provided in the standard header <random>. It includes std::random_device which provides a way to get actually random seeds, powerful pseudo random number generators like std::mt19937 and facilities to map the resulting random sequences to integer or float ranges without introducing unnecessary bias.
Here is an example how to randomly roll a die in C++11:
#include <random>
#include <iostream>
int main()
{
std::random_device rd;
std::mt19937 gen(rd());
std::uniform_int_distribution<> dis(1, 6);
for (int n=0; n<10; ++n)
std::cout << dis(gen) << ' ';
std::cout << '\n';
}
(Code from cppr) Note: std::random_device does not work properly with MinGW, at least in the version (Nuwen MinGW5.3) I tested!
It should also be noted that the state space of a mt19937 is much larger than the 32 bit we (commonly) get out of a single call to random_device. Again, this will most likely not matter for toy programs and homework, but for reference: Here is my attempt to properly seed the entire state space, plus some helpful suggestions in the answers.
If you are interested in more details about rand vs <random>, this is an interesting watch.
First line:
srand() is a pseudo-random number generator. In your case it is initialized with the current time (execution time) on your system.
Second line:
After the pseudo-random number generator is configured, you can retrieve random numbers by calling rand().
I'm trying to find a random number generator that will give me a single random number each time I run it. I have spent a week trying dozens of different ones, both from this site and others. Every time I run it, it gives me the same number! The only time it changes is if I change the range, and then it just gives me the new number over and over.
I am running Code::Blocks ver. 16.01 on Windows 7. Can anyone help?? I'm at my wits' end!
This code gives me a decently ramdom string of numbers, but still the same string each time!
#include <iostream>
#include <random>
int main()
{
std::random_device rd;
std::mt19937 eng(rd()); std::uniform_int_distribution<> distr(0, 10);
for(int n=0; n<100; ++n)
std::cout << distr(eng) << '\t';
}
I have tried the code on my compiler app on my phone as well.
Every pseudo random number generator will return the same sequence of numbers for the same initial seed value.
What you want to do is to use a different seed every time you run the program. Otherwise you'll just be using the same default seed every time and get the same values.
Picking good seeds is not as easy as you might think. Using the output from time(nullptr) for example still gives the same results if two copies of the program run within the same second. Using the value of getpid() is also bad since pid values wrap and thus sometimes you'll get the same value for different runs. Luckily you have other options. std::seed_seq lets you combine multiple bad sources and returns a good (or rather, pretty good) seed value you can use. There is also std::random_device which (on all sane implementations) returns raw entropy - perfect for seeding a pseudo random generator (or you can just use it directly if it is fast enough for your purpose) or you can combine it with std::seed_seq and the bad sources to seed a generator if you are worried it might be implemented as a prng on your implementation.
I would advice you to read this page: http://en.cppreference.com/w/cpp/numeric/random for an overview of how to deal with random number generation in modern C++.
The standard allows std::random_device to be implemented in terms of a pseudo-random number generator if there is no real random source on the system.
You may need to find a different entropy source, such as the time, or user touch co-ordinates.
Okay I'm starting to lose my mind. All I want to do is random a number between 0 and 410, and according to this page, my code should do that. And since I want a random number and not a pseudo-random number, I'm using srand() as well, in a way that e.g. this thread told me to do. But this isn't working. All I get is a number that is depending on how long it was since my last execution. If I e.g. execute it again as fast as I can, the number is usually 6 numbers higher than the last number, and if I wait longer, it's higher, etc. When it reaches 410 it goes back to 0 and begins all over again. What am I missing?
Edit: And oh, if I remove the srand(time(NULL)); line I just get the same number (41) every time I run the program. That's not even pseudo random, that's just a static number. Just copying the first line of code from the article I linked to above still gives me number 41 all the time. Am I the star in a sequel to "The Number 23", or have I missed something?
int main(void) {
srand(time(NULL));
int number = rand() % 410;
std::cout << number << std::endl;
system("pause");
}
That is what you get for using deprecated random number generation.
rand produces a fixed sequence of numbers (which by itself is fine), and does that very, very badly.
You tell rand via srand where in the sequence to start. Since your "starting point" (called seed btw) depends on the number of seconds since 1.1.1970 0:00:00 UTC, your output is obviously time depended.
The correct way to do what you want to do is using the C++11 <random> library. In your concrete example, this would look somewhat like this:
std::mt19937 rng (std::random_device{}());
std::uniform_int_distribution<> dist (0, 409);
auto random_number = dist(rng);
For more information on the evils of rand and the advantages of <random> have a look at this.
As a last remark, seeding std::mt19937 like I did above is not quite optimal because the MT's state space is much larger than the 32 bit you get out of a single call to std::random_device{}(). This is not a problem for toy programs and your standard school assignments, but for reference: Here is my take at seeding the MT's entire state space, plus some helpful suggestions in the answers.
From manual:
time() returns the time as the number of seconds since the Epoch,
1970-01-01 00:00:00 +0000 (UTC).
Which means that if you start your program twice both times at the same second you will initialize srand with same value and will get same state of PRNG.
And if you remove initialization via call to srand you will always get exactly same sequence of numbers from rand.
I'm afraid you can't get trully random numbers there. Built in functions are meant to provide just pseudo random numbers. Moreover using srand and rand, because the first uses the same approach as the second one. If you want to cook true random numbers, you must find a correct source of entrophy, working for example with atmospheric noise, as the approach of www.random.org.
The problem here consists in the seed used by the randomness algorithm: if it's a number provided by a machine, it can't be unpredictable. A normal solution for this is using external hardware.
Unfortunately you can't get a real random number from a computer without specific hardware (which is often too slow to be practical).
Therefore you need to make do with a pseudo generator. But you need to use them carefully.
The function rand is designed to return a number between 0 and RAND_MAX in a way that, broadly speaking, satisfies the statistical properties of a uniform distribution. At best you can expect the mean of the drawn numbers to be 0.5 * RAND_MAX and the variance to be RAND_MAX * RAND_MAX / 12.
Typically the implementation of rand is a linear congruential generator which basically means that the returned number is a function of the previous number. That can give surprisingly good results and allows you to seed the generator with a function srand.
But repeated use of srand ruins the statistical properties of the generator, which is what is happening to you: your use of srand is correlated with your system clock time. The behaviour you're observing is completely expected.
What you should do is to only make one call to srand and then draw a sequence of numbers using rand. You cannot easily do this in the way you've set things up. But there are alternatives; you could switch to a random number generator (say mersenne twister) which allows you to draw the (n)th term and you could pass the value of n as a command line argument.
As a final remark, I'd avoid using a modulus when drawing a number. This will create a statistical bias if your modulo is not a multiple of RAND_MAX.
Try by change the NULL in time(NULL) by time(0) (that will give you the current système time). If it doesn't work, you could try to convert time(0) into ms by doing time(0)*1000.
This comment, which states:
srand(time(0)); I would put this line as the first line in main()
instead if calling it multiple times (which will actually lead to less
random numbers).
...and I've bolded the line which I'm having an issue with... repeats common advice to call srand once in a program. Questions like srand() — why call only once? re-iterate that because time(0) returns the current time in seconds, that multiple calls to srand within the same second will produce the same seed. A common workaround is to use milliseconds or nanoseconds instead.
However, I don't understand why this means that srand should or can only be called once, or how it leads to less random numbers.
cppreference:
Generally speaking, the pseudo-random number generator should only be
seeded once, before any calls to rand(), and the start of the program.
It should not be repeatedly seeded, or reseeded every time you wish to generate a new batch of pseudo-random numbers.
phoxis's answer to srand() — why call only once?:
Initializing once the initial state with the seed value will generate
enough random numbers as you do not set the internal state with srand,
thus making the numbers more probable to be random.
Perhaps they're simply using imprecise language, none of the explanations seem to explain why calling srand multiple times is bad (aside from producing the same sequence of random numbers) or how it affects the "randomness" of the numbers. Can somebody clear this up for me?
Look at the source of srand() from this question: Rand Implementation
Also, example implementation from this thread:
static unsigned long int next = 1;
int rand(void) // RAND_MAX assumed to be 32767
{
next = next * 1103515245 + 12345;
return (unsigned int)(next/65536) % 32768;
}
void srand(unsigned int seed)
{
next = seed;
}
As you can see, when you calling srand(time(0)) you will got new numbers on rand() depends on seed. Numbers will repeat after some milions, but calling srand again will make it other. Anyway, it must repeat after some cycles - but order depends on argument for srand. This is why C rand isn't good for cryptography - you can predict next number when you know seed.
If you have fast loop, calling srand every iteration is without sense - you can got same number while your time() (1 second is very big time for modern CPUs) give another seed.
There is no reason in simple app to call srand multiple times - this generator are weak by design and if you want real random numbers, you must use other (the best I know is Blum Blum Shub)
For me, there is no more or less random numbers - it always depends on seed, and they repeat if you use same seed. Using time is good solution because it's easy to implement, but you must use only one (at beginning of main()) or when you sure that you calling srand(time(0)) in another second.
The numbers rand() returns are not actually random but "pseudo-random." What this means is that rand() generates a stream of numbers that look random for given values of "look" and "random" from an internal state that changes with each call.
As a rule, rand() is what is called a linear congruental generator, which means that uses a mechanism roughly like this:
int state; // persistent state
int rand() {
state = (a * state + b) % c;
return state;
}
with carefully chosen constants a, b and c. c tends to be a power of two in practice because that makes it faster to calculate.
The "randomness" of this sequence depends in part on the persistence of the state. If the sequence is constantly reseeded with predictable values, the return values of rand() become predictable in turn. How critical this is depends on the application, but it is not a purely academical consideration. Consider, for example, the case
a = 69069
b = 1
c = 2^32
which was used, for example, by old versions of glibc. Granted that I picked this example for the obviousness of the pattern, but the point remains in less obvious cases. Imagine this RNG were seeded with a sequence of incrementing numbers n, n+1, n+2 and so forth -- you will get from rand() a sequence of numbers, each 69069 larger than the last (modulo 2^32). The pattern will be plainly visible. Starting with 0, we would get
1
69070
138139
207208
...
rising until a bit over 4 billion in steady increments. And to make matters worse, some implementation actually returned the seed value in the first call of rand after a call to srand, in which case you'd just get your seeds back.
A pseudo random generator is an engine which produce numbers that look almost random. However, they are completely deterministic. In other words, given a seed x0, they are produced by repeated application of some injective function on x0, call it f(x0), so that f^m(x0) is quite different from f^{m-1}(x0) or f^{m+1}(x0), where the notation f^m denotes the function composition m times. In other words, f(x) has huge jumps, almost uncorrelated with the previous ones.
If you use sradnd(time) multiple times in a second, you may get the same seed, as the clock is not as fast as you may imagine. So the resulting sequence of random numbers will be the same. And this may be a (huge) problem, especially in cryptography applications (anyway, in the latter case, people buy good number generators based on real-time physical processes such as temperature difference in atmospheric data etc, or, recently, on measuring quantum bits, e.g. superposition of polarized photons, the latter being truly random, as long as quantum mechanics is correct.)
There are also other serious issues with rand. One of it is that the distribution is biased. See e.g. http://eternallyconfuzzled.com/arts/jsw_art_rand.aspx for some discussion, although I remember I've seen something similar on SO, although cannot find it now.
If you plan to use it in crypto applications, just don't do it. Use <random> and a serious random engine like Mersene's twister std::mt19937 combined with std::random_device
If you seed your random number generator twice using srand, and get different seeds, then
you will get two sequences that will be quite different. This may be satisfactory for you. However, each sequence per se will not be a good random distribution due to the issues I mentioned above. On the other hand, if you seed your rng too many times, you will get the same seed, and THIS IS BAD, as you'll generate the same numbers over and over again.
PS: seen in the comments that pseudo-numbers depend on a seed, and this is bad. This is the definition of pseudo-numbers, and it is not a bad thing as it allows you to repeat numerical experiments with the same sequence. The idea is that each different seed should produce a sequence of (almost) random numbers, different from a previous sequence (technically, you shouldn't be able to distinguish them from a perfect random sequence).
The seed determines what random numbers will be generated, in order, i.e. srand(1), will always generate the same number on the first call to rand(), the same on the second call to rand() and so on.
In other words, if you re-seeded with the same seed before each rand() invocation, you'd generate the same random number every single time.
So successive seeding with time(0), during a single second, will mean all your random numbers after re-seeding are actually the same number.
Most of the other answers are saying exactly what the question already stated: multiple calls to srand with the same second will produce the same seed. I believe the actual question is the same one that I had, which is: why would it be bad to call srand multiple times, even if it was with a different seed every time?
I can think of three reasons:
People are not clear in their language and they actually mean srand should not be called multiple times with time() if you want different sequences of random numbers.
It's cryptographically bad because every seed passed to srand is not itself a random number (well, it's probably not). Meaning, every srand is injecting a chance for someone to guess that seed and therefore predict your stream of pseudo-random numbers.
It can mess up the distribution of pseudo-random numbers. #vsoftco's answer gave me a clue. If you call srand once, rand can be designed to give you a uniform distribution of pseudo-random numbers over its lifetime. If you call srand in the middle, however, you'll throw off that uniform distribution because it would "start over" with a new seed.
So, if you don't care about any of that, I would think it's okay to call srand more than once. In my case, I want to call it at the start of my program, but call it again after a fork() because the seed is apparently shared across child processes, and I want each child process to have its own sequence of pseudo-random numbers.
Going back to why it's cryptographically bad, it's easier to guess a seed if it's something like time() because a bad actor can try to guess the time it was seeded. That is why calling srand at the start of a program might be better, because it could be less likely that someone would guess that time as well as, say, when a server request was initiated.
But I would surmise that even passing nanoseconds would be cryptographically dangerous if there's a chance the underlying clock doesn't have that kind of precision. Imagine, for example, that you call srand(get_time_in_ns()) and the underlying clock only returns time to the nearest millisecond.
Now, I'm no crypto expert in any way, but this leads me to wonder if it would be safer than current-time to pass the output of a different pseudo-random generator as seeds to multiple srand calls? For example, can you call each srand with a number from Linux's /dev/random? (I imagine you might want to do that if you want a safer seed than the current time but still want to use rand() so you don't have the overhead of reading from the kernel every time.)
I am relative new to c and c++. In java, the language I am used to program in, its very easy to implement random number generation. Just call the the static random-method from a class called Math.
int face = ((int)(Math.random() * 6) + 1);
simulates a dice-throw ...
In c and c++ you have to "seed the random number generator" , by calling the srand-function
srand ( time(NULL) );
What is the point of doing this - I mean is there any advantage of having to seed the random number generator every time the code is run?
Given the same seed, a pseudo random number generator will produce the same sequence every time. So it comes down to whether you want a different sequence of pseudo random numbers each time you run, or not.
It really depends on your needs. There are times when you want to repeat a sequence. And times when you do not. You need to understand the needs of each specific application.
One thing you must never do is seed repeatedly during generation of a single sequence. Doing so very likely will destroy the distribution of your sequence.
What's usually called a random number generator is actually a pseudo-random number generator. This typically means that you can generate the same random sequence if you provide the "key" to that sequence, referred to as the "seed". This is very useful when you wish to test your algorithm that is based on randomization, and you need to ensure repeatable results.
If you do not "seed" your Random number generator, it is seeded with some (usually based on system time) random number by default, and therefore produces the different sequence every time that you run your program.
If you don't seed the generator, it will have the same seed every time you run your program, and the random number sequence will be the same each time.
Also note that you only should to seed the generator once, at the beginning of the program.
The advantage is that you can repeat a random number sequence by supplying the same seed.
The game Elite used this to store an entire world, consisting of thousands of stars, as a single number.
To generate the exact same world a second time, the just supplied the same seed.
The seed is needed for pseudo random number generator to generate different random number sequence from previous ones (not always). If you do not want the repeated sequence then you need to seed the pseudo random number generator.
Try these codes and see the difference.
Without seed:
#include <stdio.h>
#include <stdlib.h>
int main()
{
printf("%d", rand()%6 + 1);
}
With seed:
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
int main()
{
srand(time(NULL));
printf("%d", rand()%6 + 1);
}
The random number generator is not truly random: say you seed it with 12 and make 100 random numbers, repeat the process and seed it with 12 again and make another 100 random numbers, they will be the same.
I have attached a small sample of 2 runs at 20 entries each with the seed of 12 to illustrate, immediately after the code which created them:
#include <iostream>
#include <cstdlib>
using namespace std;
int main()
{
srand(12);
for (int i =0;i<100; i++)
{
cout << rand() << endl;
}
return 0;
}
To avoid such repetition it is commonplace to use a more unique value, and as the time is always changing, and the chances of a two programs generating random sequences at exactly the same time are slim (especially when at the millisecond level), one can reasonably safely use time as an almost unique seed.
Requirements: seeding only need take place once per unique random sequence you need to generate.
However, there is an unexpected up-/down-side to this:
if the exact time is known of when the first sequence is generated then the exact sequence can be re-generated in the future by entering the seed value manually, causing the random number generator to step through its process in the same fashion as before (this is an upside for storing random sequences, and a downside for preserving their randomness).
In C/C++, a dice roll would be simulated like this:
int face = (rand() % 6) + 1);
^
|___________ Modulo operator
The % 6 limits the random number to 0 through 5 and the + 1 is made to offset the limit, so it becomes 1 through 6.